Develop a simple linear regression equation to forecast annual sales. For this? regression, the dependent? variable, Y, is the demand in each year and the independent? variable, X, is the index for the year? (i.e., X = 1 for year? 1, X = 2 for year? 2, and so on until X = 5 for year? 5).The regression is given by the equation Y= __+__X (round to one decimal place)

  Additional data on the monthly sales of 10-ounce shampoo bottles for the past 5 years is below:

 

Transcribed Image Text:

Cannister, Inc., specializes in the manufacture of plastic containers. Click the icon to view the data on the monthly sales of 10-ounce shampoo bottles for the past five years. a. Using the multiplicative seasonal method, calculate the monthly seasonal indices. (Enter your responses rounded to three decimal places.) Average seasonal index 0.783 0.697 0.860 0.927 0.992 1.110 1.197 1.318 1.168 1.173 0.955 0.821 Month January February March April May June July August September October November December b. Develop a simple linear regression equation to forecast annual sales. For this regression, the dependent variable, Y, is the demand in each year and the independent variable, X. is the index for the year (i.e.. X = 1 for year 1, X=2 for year 2, and so on until X = 5 for year 5). The regression is given by the equation Y = + X. (Enter your responses rounded to one decimal place.) Year January February March 1 774 687 868 912 960 1,047 July 1,173 August 1,300 September 1,122 October 1,102 924 746 April May June November December 773 690 866 946 1,029 1,163 1,298 1,433 1,255 1,267 1,019 864 3 928 783 977 1,069 1,134 1,266 1,311 1,520 1,387 1,424 1,123 986 4 983 851 1,030 1,123 1,204 1,362 1,494 1,639 1,518 1,571 1,356 1,172 5 1,062 1,022 1,218 1,299 1,398 1,577 1,619 1,692 1,442 1,391 1,072 976 0 Cannister, Inc., specializes in the manufacture of plastic containers. Click the icon to view the data on the monthly sales of 10-ounce shampoo bottles for the past five years. a. Using the multiplicative seasonal method, calculate the monthly seasonal indices. (Enter your responses rounded to three decimal places.) Average seasonal index 0.783 0.697 0.860 0.927 0.992 1.110 1.197 1.318 1.168 1.173 0.955 0.821 Month January February March April May June July August September October November December b. Develop a simple linear regression equation to forecast annual sales. For this regression, the dependent variable, Y, is the demand in each year and the independent variable, X. is the index for the year (i.e.. X = 1 for year 1, X=2 for year 2, and so on until X = 5 for year 5). The regression is given by the equation Y = + X. (Enter your responses rounded to one decimal place.) Year January February March 1 774 687 868 912 960 1,047 July 1,173 August 1,300 September 1,122 October 1,102 924 746 April May June November December 773 690 866 946 1,029 1,163 1,298 1,433 1,255 1,267 1,019 864 3 928 783 977 1,069 1,134 1,266 1,311 1,520 1,387 1,424 1,123 986 4 983 851 1,030 1,123 1,204 1,362 1,494 1,639 1,518 1,571 1,356 1,172 5 1,062 1,022 1,218 1,299 1,398 1,577 1,619 1,692 1,442 1,391 1,072 976 0

Answer rating: 100% (QA)

Annual demand for year 1 77468786891296010471173130011221102924746 11615 Annua... View the full answer